{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import math\n",
    "\n",
    "from IPython import display\n",
    "from matplotlib import cm\n",
    "from matplotlib import gridspec\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import metrics\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.data import Dataset\n",
    "\n",
    "tf.logging.set_verbosity(tf.logging.ERROR)\n",
    "pd.options.display.max_rows = 10\n",
    "pd.options.display.float_format = '{:.1f}'.format\n",
    "\n",
    "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
    "\n",
    "# california_housing_dataframe = california_housing_dataframe.reindex(\n",
    "#     np.random.permutation(california_housing_dataframe.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_features(california_housing_dataframe):\n",
    "  \"\"\"Prepares input features from California housing data set.\n",
    "\n",
    "  Args:\n",
    "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
    "      from the California housing data set.\n",
    "  Returns:\n",
    "    A DataFrame that contains the features to be used for the model, including\n",
    "    synthetic features.\n",
    "  \"\"\"\n",
    "  selected_features = california_housing_dataframe[\n",
    "    [\"latitude\",\n",
    "     \"longitude\",\n",
    "     \"housing_median_age\",\n",
    "     \"total_rooms\",\n",
    "     \"total_bedrooms\",\n",
    "     \"population\",\n",
    "     \"households\",\n",
    "     \"median_income\"]]\n",
    "  processed_features = selected_features.copy()\n",
    "  # Create a synthetic feature.\n",
    "  processed_features[\"rooms_per_person\"] = (\n",
    "    california_housing_dataframe[\"total_rooms\"] /\n",
    "    california_housing_dataframe[\"population\"])\n",
    "  return processed_features\n",
    "\n",
    "def preprocess_targets(california_housing_dataframe):\n",
    "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
    "\n",
    "  Args:\n",
    "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
    "      from the California housing data set.\n",
    "  Returns:\n",
    "    A DataFrame that contains the target feature.\n",
    "  \"\"\"\n",
    "  output_targets = pd.DataFrame()\n",
    "  # Scale the target to be in units of thousands of dollars.\n",
    "  output_targets[\"median_house_value\"] = (\n",
    "    california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
    "  return output_targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>rooms_per_person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34.6</td>\n",
       "      <td>-118.5</td>\n",
       "      <td>27.5</td>\n",
       "      <td>2655.7</td>\n",
       "      <td>547.1</td>\n",
       "      <td>1476.0</td>\n",
       "      <td>505.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>12.1</td>\n",
       "      <td>2258.1</td>\n",
       "      <td>434.3</td>\n",
       "      <td>1174.3</td>\n",
       "      <td>391.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>32.5</td>\n",
       "      <td>-121.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.8</td>\n",
       "      <td>-118.9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1451.8</td>\n",
       "      <td>299.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>34.0</td>\n",
       "      <td>-118.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2113.5</td>\n",
       "      <td>438.0</td>\n",
       "      <td>1207.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>34.4</td>\n",
       "      <td>-117.8</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3146.0</td>\n",
       "      <td>653.0</td>\n",
       "      <td>1777.0</td>\n",
       "      <td>606.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>41.8</td>\n",
       "      <td>-114.3</td>\n",
       "      <td>52.0</td>\n",
       "      <td>37937.0</td>\n",
       "      <td>5471.0</td>\n",
       "      <td>35682.0</td>\n",
       "      <td>5189.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>55.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
       "mean       34.6     -118.5                27.5       2655.7           547.1   \n",
       "std         1.6        1.2                12.1       2258.1           434.3   \n",
       "min        32.5     -121.4                 1.0          2.0             2.0   \n",
       "25%        33.8     -118.9                17.0       1451.8           299.0   \n",
       "50%        34.0     -118.2                28.0       2113.5           438.0   \n",
       "75%        34.4     -117.8                36.0       3146.0           653.0   \n",
       "max        41.8     -114.3                52.0      37937.0          5471.0   \n",
       "\n",
       "       population  households  median_income  rooms_per_person  \n",
       "count     12000.0     12000.0        12000.0           12000.0  \n",
       "mean       1476.0       505.4            3.8               1.9  \n",
       "std        1174.3       391.7            1.9               1.3  \n",
       "min           3.0         2.0            0.5               0.0  \n",
       "25%         815.0       283.0            2.5               1.4  \n",
       "50%        1207.0       411.0            3.5               1.9  \n",
       "75%        1777.0       606.0            4.6               2.3  \n",
       "max       35682.0      5189.0           15.0              55.2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
    "training_examples.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>111.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>117.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>170.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>244.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       median_house_value\n",
       "count             12000.0\n",
       "mean                198.0\n",
       "std                 111.9\n",
       "min                  15.0\n",
       "25%                 117.1\n",
       "50%                 170.5\n",
       "75%                 244.4\n",
       "max                 500.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
    "training_targets.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>rooms_per_person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.1</td>\n",
       "      <td>-122.2</td>\n",
       "      <td>31.3</td>\n",
       "      <td>2614.8</td>\n",
       "      <td>521.1</td>\n",
       "      <td>1318.1</td>\n",
       "      <td>491.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>13.4</td>\n",
       "      <td>1979.6</td>\n",
       "      <td>388.5</td>\n",
       "      <td>1073.7</td>\n",
       "      <td>366.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>36.1</td>\n",
       "      <td>-124.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>37.5</td>\n",
       "      <td>-122.4</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1481.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.8</td>\n",
       "      <td>-122.1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2164.0</td>\n",
       "      <td>424.0</td>\n",
       "      <td>1074.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38.4</td>\n",
       "      <td>-121.9</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3161.2</td>\n",
       "      <td>635.0</td>\n",
       "      <td>1590.2</td>\n",
       "      <td>603.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42.0</td>\n",
       "      <td>-121.4</td>\n",
       "      <td>52.0</td>\n",
       "      <td>32627.0</td>\n",
       "      <td>6445.0</td>\n",
       "      <td>28566.0</td>\n",
       "      <td>6082.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
       "mean       38.1     -122.2                31.3       2614.8           521.1   \n",
       "std         0.9        0.5                13.4       1979.6           388.5   \n",
       "min        36.1     -124.3                 1.0          8.0             1.0   \n",
       "25%        37.5     -122.4                20.0       1481.0           292.0   \n",
       "50%        37.8     -122.1                31.0       2164.0           424.0   \n",
       "75%        38.4     -121.9                42.0       3161.2           635.0   \n",
       "max        42.0     -121.4                52.0      32627.0          6445.0   \n",
       "\n",
       "       population  households  median_income  rooms_per_person  \n",
       "count      5000.0      5000.0         5000.0            5000.0  \n",
       "mean       1318.1       491.2            4.1               2.1  \n",
       "std        1073.7       366.5            2.0               0.6  \n",
       "min           8.0         1.0            0.5               0.1  \n",
       "25%         731.0       278.0            2.7               1.7  \n",
       "50%        1074.0       403.0            3.7               2.1  \n",
       "75%        1590.2       603.0            5.1               2.4  \n",
       "max       28566.0      6082.0           15.0              18.3  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
    "validation_examples.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>229.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>122.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>130.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>213.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>303.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       median_house_value\n",
       "count              5000.0\n",
       "mean                229.5\n",
       "std                 122.5\n",
       "min                  15.0\n",
       "25%                 130.4\n",
       "50%                 213.0\n",
       "75%                 303.2\n",
       "max                 500.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
    "validation_targets.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assignment 1: 检查数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assignment 2: 绘制纬度/经度与房屋价值中位数的曲线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13, 8))\n",
    "\n",
    "ax = plt.subplot(1, 2, 1)\n",
    "ax.set_title(\"Validation Data\")\n",
    "\n",
    "ax.set_autoscaley_on(False)\n",
    "ax.set_ylim([32, 43])\n",
    "ax.set_autoscalex_on(False)\n",
    "ax.set_xlim([-126, -112])\n",
    "plt.scatter(validation_examples[\"longitude\"],\n",
    "            validation_examples[\"latitude\"],\n",
    "            cmap=\"coolwarm\",\n",
    "            c=validation_targets[\"median_house_value\"] / validation_targets[\"median_house_value\"].max())\n",
    "\n",
    "ax = plt.subplot(1,2,2)\n",
    "ax.set_title(\"Training Data\")\n",
    "\n",
    "ax.set_autoscaley_on(False)\n",
    "ax.set_ylim([32, 43])\n",
    "ax.set_autoscalex_on(False)\n",
    "ax.set_xlim([-126, -112])\n",
    "plt.scatter(training_examples[\"longitude\"],\n",
    "            training_examples[\"latitude\"],\n",
    "            cmap=\"coolwarm\",\n",
    "            c=training_targets[\"median_house_value\"] / training_targets[\"median_house_value\"].max())\n",
    "_ = plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "添加对数据集的随机处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "california_housing_dataframe = california_housing_dataframe.reindex(\n",
    "    np.random.permutation(california_housing_dataframe.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>206.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>115.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>119.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>179.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>264.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       median_house_value\n",
       "count             12000.0\n",
       "mean                206.8\n",
       "std                 115.3\n",
       "min                  15.0\n",
       "25%                 119.3\n",
       "50%                 179.8\n",
       "75%                 264.4\n",
       "max                 500.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
    "training_targets.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>208.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>117.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>120.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>181.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>267.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       median_house_value\n",
       "count              5000.0\n",
       "mean                208.6\n",
       "std                 117.5\n",
       "min                  15.0\n",
       "25%                 120.1\n",
       "50%                 181.3\n",
       "75%                 267.1\n",
       "max                 500.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
    "validation_targets.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13, 8))\n",
    "\n",
    "ax = plt.subplot(1, 2, 1)\n",
    "ax.set_title(\"Validation Data\")\n",
    "\n",
    "ax.set_autoscaley_on(False)\n",
    "ax.set_ylim([32, 43])\n",
    "ax.set_autoscalex_on(False)\n",
    "ax.set_xlim([-126, -112])\n",
    "plt.scatter(validation_examples[\"longitude\"],\n",
    "            validation_examples[\"latitude\"],\n",
    "            cmap=\"coolwarm\",\n",
    "            c=validation_targets[\"median_house_value\"] / validation_targets[\"median_house_value\"].max())\n",
    "\n",
    "ax = plt.subplot(1,2,2)\n",
    "ax.set_title(\"Training Data\")\n",
    "\n",
    "ax.set_autoscaley_on(False)\n",
    "ax.set_ylim([32, 43])\n",
    "ax.set_autoscalex_on(False)\n",
    "ax.set_xlim([-126, -112])\n",
    "plt.scatter(training_examples[\"longitude\"],\n",
    "            training_examples[\"latitude\"],\n",
    "            cmap=\"coolwarm\",\n",
    "            c=training_targets[\"median_house_value\"] / training_targets[\"median_house_value\"].max())\n",
    "_ = plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assignment 4: 训练和评估模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
    "    \"\"\"Trains a linear regression model of one feature.\n",
    "  \n",
    "    Args:\n",
    "      features: pandas DataFrame of features\n",
    "      targets: pandas DataFrame of targets\n",
    "      batch_size: Size of batches to be passed to the model\n",
    "      shuffle: True or False. Whether to shuffle the data.\n",
    "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
    "    Returns:\n",
    "      Tuple of (features, labels) for next data batch\n",
    "    \"\"\"\n",
    "    \n",
    "    # Convert pandas data into a dict of np arrays.\n",
    "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
    " \n",
    "    # Construct a dataset, and configure batching/repeating\n",
    "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
    "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
    "    \n",
    "    # Shuffle the data, if specified\n",
    "    if shuffle:\n",
    "      ds = ds.shuffle(10000)\n",
    "    \n",
    "    # Return the next batch of data\n",
    "    features, labels = ds.make_one_shot_iterator().get_next()\n",
    "    return features, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def construct_feature_columns(input_features):\n",
    "  \"\"\"Construct the TensorFlow Feature Columns.\n",
    "\n",
    "  Args:\n",
    "    input_features: The names of the numerical input features to use.\n",
    "  Returns:\n",
    "    A set of feature columns\n",
    "  \"\"\" \n",
    "  return set([tf.feature_column.numeric_column(my_feature)\n",
    "              for my_feature in input_features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(\n",
    "    learning_rate,\n",
    "    steps,\n",
    "    batch_size,\n",
    "    training_examples,\n",
    "    training_targets,\n",
    "    validation_examples,\n",
    "    validation_targets):\n",
    "  \"\"\"Trains a linear regression model of one feature.\n",
    "  \n",
    "  In addition to training, this function also prints training progress information,\n",
    "  as well as a plot of the training and validation loss over time.\n",
    "  \n",
    "  Args:\n",
    "    learning_rate: A `float`, the learning rate.\n",
    "    steps: A non-zero `int`, the total number of training steps. A training step\n",
    "      consists of a forward and backward pass using a single batch.\n",
    "    batch_size: A non-zero `int`, the batch size.\n",
    "    training_examples: A `DataFrame` containing one or more columns from\n",
    "      `california_housing_dataframe` to use as input features for training.\n",
    "    training_targets: A `DataFrame` containing exactly one column from\n",
    "      `california_housing_dataframe` to use as target for training.\n",
    "    validation_examples: A `DataFrame` containing one or more columns from\n",
    "      `california_housing_dataframe` to use as input features for validation.\n",
    "    validation_targets: A `DataFrame` containing exactly one column from\n",
    "      `california_housing_dataframe` to use as target for validation.\n",
    "      \n",
    "  Returns:\n",
    "    A `LinearRegressor` object trained on the training data.\n",
    "  \"\"\"\n",
    "\n",
    "  periods = 10\n",
    "  steps_per_period = steps / periods\n",
    "  \n",
    "  # Create a linear regressor object.\n",
    "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
    "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
    "  linear_regressor = tf.estimator.LinearRegressor(\n",
    "      feature_columns=construct_feature_columns(training_examples),\n",
    "      optimizer=my_optimizer\n",
    "  )\n",
    "  \n",
    "  # 1. Create input functions.\n",
    "  training_input_fn = lambda: my_input_fn(\n",
    "      training_examples, \n",
    "      training_targets[\"median_house_value\"], \n",
    "      batch_size=batch_size)\n",
    "  predict_training_input_fn = lambda: my_input_fn(\n",
    "      training_examples, \n",
    "      training_targets[\"median_house_value\"], \n",
    "      num_epochs=1, \n",
    "      shuffle=False)\n",
    "  predict_validation_input_fn = lambda: my_input_fn(\n",
    "      validation_examples, validation_targets[\"median_house_value\"], \n",
    "      num_epochs=1, \n",
    "      shuffle=False)\n",
    "  \n",
    "  # Train the model, but do so inside a loop so that we can periodically assess\n",
    "  # loss metrics.\n",
    "  print(\"Training model...\")\n",
    "  print(\"RMSE (on training data):\")\n",
    "  training_rmse = []\n",
    "  validation_rmse = []\n",
    "  for period in range (0, periods):\n",
    "    # Train the model, starting from the prior state.\n",
    "    linear_regressor.train(\n",
    "        input_fn=training_input_fn,\n",
    "        steps=steps_per_period,\n",
    "    )\n",
    "    # 2. Take a break and compute predictions.\n",
    "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
    "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
    "    \n",
    "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
    "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
    "    \n",
    "    # Compute training and validation loss.\n",
    "    training_root_mean_squared_error = math.sqrt(\n",
    "        metrics.mean_squared_error(training_predictions, training_targets))\n",
    "    validation_root_mean_squared_error = math.sqrt(\n",
    "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
    "    # Occasionally print the current loss.\n",
    "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
    "    # Add the loss metrics from this period to our list.\n",
    "    training_rmse.append(training_root_mean_squared_error)\n",
    "    validation_rmse.append(validation_root_mean_squared_error)\n",
    "  print(\"Model training finished.\")\n",
    "\n",
    "  # Output a graph of loss metrics over periods.\n",
    "  plt.ylabel(\"RMSE\")\n",
    "  plt.xlabel(\"Periods\")\n",
    "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
    "  plt.tight_layout()\n",
    "  plt.plot(training_rmse, label=\"training\")\n",
    "  plt.plot(validation_rmse, label=\"validation\")\n",
    "  plt.legend()\n",
    "\n",
    "  return linear_regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model...\n",
      "RMSE (on training data):\n",
      "  period 00 : 216.64\n",
      "  period 01 : 198.95\n",
      "  period 02 : 185.40\n",
      "  period 03 : 176.41\n",
      "  period 04 : 171.04\n",
      "  period 05 : 167.81\n",
      "  period 06 : 166.75\n",
      "  period 07 : 166.71\n",
      "  period 08 : 167.47\n",
      "  period 09 : 168.29\n",
      "Model training finished.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_regressor = train_model(\n",
    "    learning_rate=0.00003,\n",
    "    steps=500,\n",
    "    batch_size=5,\n",
    "    training_examples=training_examples,\n",
    "    training_targets=training_targets,\n",
    "    validation_examples=validation_examples,\n",
    "    validation_targets=validation_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assignment 5: 基于测试数据进行评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
    "\n",
    "test_examples = preprocess_features(california_housing_test_data)\n",
    "test_targets = preprocess_targets(california_housing_test_data)\n",
    "\n",
    "predict_test_input_fn = lambda: my_input_fn(\n",
    "      test_examples, \n",
    "      test_targets[\"median_house_value\"], \n",
    "      num_epochs=1, \n",
    "      shuffle=False)\n",
    "\n",
    "test_predictions = linear_regressor.predict(input_fn=predict_test_input_fn)\n",
    "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n",
    "\n",
    "root_mean_squared_error = math.sqrt(\n",
    "    metrics.mean_squared_error(test_predictions, test_targets))\n",
    "\n",
    "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
